Real-Time Fraud Detection: Stop Credit Card Fraud wtih Real-Time Data Analytics

To Catch Fraud, Real-Time, Analytics-Based Detection is Key

Catching fraud after it has occurred is good. Real-time fraud detection – which can be achieved by leveraging real-time data analytics – is much, much better. Here’s why.

You should always be on the lookout for fraud. Even if the fraud has happened in the past, identifying and investigating fraudulent events helps you to understand the weaknesses in your systems that attackers use to compromise data or steal resources.

Periodically reviewing reports about historical fraudulent incidents also helps you plan ways to keep yourself and your customers safer in the future.

Real-time fraud detection

Real-Time Fraud Detection is Essential

But the unfortunate fact is that detecting fraud after the event is not nearly as useful as catching it in real time.

The reason why should be obvious. Once a fraudulent act has occurred, the damage has been done. The best you can do at that point is mitigate the fallout and take steps to prevent a similar breach from occurring again.

But if you catch fraud in real time, you have the opportunity to stop it before damage occurs or is maximized.

Catching credit card fraud with real-time fraud detection

Case in Point: Credit Card Fraud

To illustrate the point, here ‘s an example: By identifying a credit card transaction that is not legitimate, you can prevent the sale before the fraudsters make off with whatever they are trying to purchase. In a few seconds, using automated tools, you can prevent fraud, saving yourself and your customer hundreds or thousands of dollars.

On the other hand, if you fail to catch the fraudulent transaction in real time, you’ll have to address it after the fact. If the purchase was made online, you could cancel the card payment, and there’s a chance you could get the merchant to cancel the order before shipping – if you act quickly.

If it was a fraudulent face-to-face transaction, you ‘re even worse off if you lack real-time fraud detection. In that case, it’s likely that the thieves will already have disappeared back into anonymity as soon as the fraudulent transaction is completed. Short of hoping the police can track them down by following paper trails or identifying them through security cameras, you have little hope of ever recovering the stolen goods.

In short, an ounce of real-time fraud detection is worth many pounds of after-the-fact fraud identification.

Download the white paper: Accessing and Integrating Mainframe Application Data with Hadoop and Spark

Data Analytics and Real-Time Fraud Detection

If you want to catch and stop fraud in real time, you need to leverage real-time data analytics.

Your chances of manually detecting fraud as it occurs, without relying on data analytics, are basically zero. Unless the fraudsters make a mistake and give themselves away, they have a very good chance of not being caught before an illegitimate transaction is complete, because it takes human beings too long to parse and interpret information that reveals fraud.

But with real-time data analytics, you can rely on automated tools to identify and react to fraud instantly. By establishing a baseline of normal transaction activity and using real-time analytics to identify anomalies that could signal fraud, you can streamline fraud detection to the point that it becomes instantaneous.

Syncsort and Real-Time Fraud Detection

More and more organizations are looking to leverage real-time data analytics to avoid a reactive approach by catching fraud after Fight it in it has occurred. Syncsort’s suite of Big Data solutions provides the tools you need for real-time, analytics-based fraud detection and mitigation.

Solutions like DMX-h allow you to access and integrate data from mainframes – the systems where the bulk of transaction information is stored in fraud-prone industries like finance – into analytics platforms like Hadoop, which can detect anomalies instantaneously.

Download Syncsort’s white paper, “Accessing and Integrating Mainframe Application Data with Hadoop and Spark,” to learn more about setting up an analytics environment that can help you catch fraud… in real time!

Christopher Tozzi

Authored by Christopher Tozzi

Christopher Tozzi has written about emerging technologies for a decade. His latest book, For Fun and Profit: A History of the Free and Open Source Software Revolution, is forthcoming with MIT Press in July 2017.

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